Abstract:

A memory-based scaling model--ANCHOR--is proposed and tested.
The perceived magnitude of the target stimulus is compared to a set of
anchors in memory. Anchor selection is probabilistic and sensitive
to similarity, base-level strength, and recency. The winning anchor provides
a reference point near the target and thereby converts the global scaling
problem into a local comparison. An explicit correction strategy determines
the final response. Two incremental learning mechanisms update the locations
and base-level activations of the anchors. This gives rise to sequential,
context, transfer, practice, and other dynamic effects. The scale unfolds as
an adaptive map. A hierarchy of models is tested on a battery of quantitative
measures from two experiments in absolute identification and category rating.